Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm

Abstract

Background: An insulin pump shutoff system can prevent nocturnal hypoglycemia and is a first step on the pathway toward a closed-loop artificial pancreas. In previous pump shutoff studies using a voting algorithm and a 1 min continuous glucose monitor (CGM), 80% of induced hypoglycemic events were prevented.

Methods: The pump shutoff algorithm used in previous studies was revised to a single Kalman filter to reduce complexity, incorporate CGMs with different sample times, handle sensor signal dropouts, and enforce safety constraints on the allowable pump shutoff time.

Results: Retrospective testing of the new algorithm on previous clinical data sets indicated that, for the four cases where the previous algorithm failed (minimum reference glucose less than 60 mg/dl), the mean suspension start time was 30 min earlier than the previous algorithm. Inpatient studies of the new algorithm have been conducted on 16 subjects. The algorithm prevented hypoglycemia in 73% of subjects. Suspension-induced hyperglycemia is not assessed, because this study forced excessive basal insulin infusion rates.

Conclusions: The new algorithm functioned well and is flexible enough to handle variable sensor sample times and sensor dropouts. It also provides a framework for handling sensor signal attenuations, which can be challenging, particularly when they occur overnight.

Figure 1

Comparison of the proposed Kalman-filter-based algorithm with the previous voting algorithm. Note that a sample time for the proposed algorithm is 5 min, while the voting algorithm was based on 1 min samples. In the horizontal lines, green represents regular basal delivery while red represents pump suspension. In this example, the proposed algorithm shuts off the pump 25 min before the voting algorithm did in the clinic, so it is likely that the proposed algorithm would have avoided hypoglycemia.

Figure 2

Comparison of the proposed Kalman-filter-based algorithm with the previous voting algorithm. In this example, the proposed algorithm shuts off the pump for 71 fewer minutes than the voting algorithm did in the clinic, largely driven by an earlier resumption of basal insulin delivery. It is likely that the glucose level would not have risen as high.

Figure 3

Example where the new algorithm shut off the pump in time to avoid hypoglycemia.

Figure 4

Example where the new algorithm did not avoid hypoglycemia. Note, however, that there is a severe sensor bias of 58 mg/dl. Based on the sensor reading, hypoglycemia was avoided.

Figure 5

Example where sensor attenuation significantly affected algorithm performance, inducing inappropriate pump suspensions.